Cellular Neural Networks or Cellular Nonlinear Networks (CNN) have been applied to many different fields and problems including, but limited to, image processing since 1988. However, most of the prior art CNN approaches are either based on software solutions (e.g., Convolutional Neural Networks, Recurrent Neural Networks, etc.) or based on hardware that are designed for other purposes (e.g., graphic processing, general computation, etc.). As a result, CNN prior approaches are too slow in term of computational speed and/or too expensive thereby impractical for processing large amount of imagery data. The imagery data can be any two-dimensional data (e.g., still photo, picture, a frame of a video stream, converted form of voice data, etc.).
Until now, prior art machine inference has been done by feeding data to a machine learning model through a long-distance cloud connection or by keeping the data and the deep learning model in a single computing system. However the requirement of a single computing system limits the commercial availability of machine learning to only a small fraction of the population, as few people own the well-trained models needed to generate reliable inferences and vast computing power to process large sets of data. Although prior art cloud solution solves the matter of computing power by transferring the data processing to a capable site. It poses two different problems to the client. First, since the data travels to a central warehouse, it is exposed to the people working with it makes it impossible to process data privately through cloud. Second, since the cloud is such a long distance away from the client, the data will be processed at a much slower pace. Therefore, it would be desirable to have an improved artificial intelligence inference computing device that overcomes the problems, shortcoming and defects described above.